Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations7043
Missing cells1526
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory220.0 B

Variable types

Text2
Categorical8
Numeric15
Boolean13

Alerts

avg_monthly_gb_download is highly overall correlated with internet_serviceHigh correlation
avg_monthly_long_distance_charges is highly overall correlated with phone_service and 1 other fieldsHigh correlation
churn_category is highly overall correlated with churn_reason and 1 other fieldsHigh correlation
churn_reason is highly overall correlated with churn_category and 1 other fieldsHigh correlation
customer_status is highly overall correlated with churn_category and 1 other fieldsHigh correlation
device_protection_plan is highly overall correlated with total_chargesHigh correlation
internet_service is highly overall correlated with avg_monthly_gb_download and 3 other fieldsHigh correlation
internet_type is highly overall correlated with internet_service and 2 other fieldsHigh correlation
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 1 other fieldsHigh correlation
married is highly overall correlated with number_of_referralsHigh correlation
monthly_charge is highly overall correlated with internet_service and 9 other fieldsHigh correlation
multiple_lines is highly overall correlated with monthly_chargeHigh correlation
number_of_referrals is highly overall correlated with marriedHigh correlation
offer is highly overall correlated with tenure_in_monthsHigh correlation
online_backup is highly overall correlated with total_chargesHigh correlation
phone_service is highly overall correlated with avg_monthly_long_distance_charges and 1 other fieldsHigh correlation
streaming_movies is highly overall correlated with monthly_charge and 3 other fieldsHigh correlation
streaming_music is highly overall correlated with monthly_charge and 1 other fieldsHigh correlation
streaming_tv is highly overall correlated with monthly_charge and 2 other fieldsHigh correlation
tenure_in_months is highly overall correlated with offer and 3 other fieldsHigh correlation
total_charges is highly overall correlated with device_protection_plan and 7 other fieldsHigh correlation
total_long_distance_charges is highly overall correlated with avg_monthly_long_distance_charges and 3 other fieldsHigh correlation
total_revenue is highly overall correlated with monthly_charge and 3 other fieldsHigh correlation
unlimited_data is highly overall correlated with internet_service and 2 other fieldsHigh correlation
zip_code is highly overall correlated with latitude and 1 other fieldsHigh correlation
phone_service is highly imbalanced (54.1%) Imbalance
churn_reason is highly imbalanced (54.1%) Imbalance
avg_monthly_gb_download has 1526 (21.7%) missing values Missing
customer_id has unique values Unique
number_of_dependents has 5416 (76.9%) zeros Zeros
number_of_referrals has 3821 (54.3%) zeros Zeros
avg_monthly_long_distance_charges has 682 (9.7%) zeros Zeros
monthly_charge has 120 (1.7%) zeros Zeros
total_refunds has 6518 (92.5%) zeros Zeros
total_extra_data_charges has 6315 (89.7%) zeros Zeros
total_long_distance_charges has 682 (9.7%) zeros Zeros

Reproduction

Analysis started2025-02-25 13:37:42.239588
Analysis finished2025-02-25 13:38:17.141037
Duration34.9 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:17.847955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7043 ?
Unique (%)100.0%

Sample

1st row0002-ORFBO
2nd row0003-MKNFE
3rd row0004-TLHLJ
4th row0011-IGKFF
5th row0013-EXCHZ
ValueCountFrequency (%)
0011-igkff 1
 
< 0.1%
9995-hotoh 1
 
< 0.1%
0002-orfbo 1
 
< 0.1%
9971-zwpbf 1
 
< 0.1%
9972-ewrjs 1
 
< 0.1%
9972-nktfd 1
 
< 0.1%
9972-vafjj 1
 
< 0.1%
9974-jfbhq 1
 
< 0.1%
9975-gpkzu 1
 
< 0.1%
9975-skrnr 1
 
< 0.1%
Other values (7033) 7033
99.9%
2025-02-25T15:38:18.400792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35215
50.0%
Decimal Number 28172
40.0%
Dash Punctuation 7043
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1442
 
4.1%
H 1396
 
4.0%
B 1393
 
4.0%
S 1386
 
3.9%
V 1382
 
3.9%
T 1374
 
3.9%
C 1368
 
3.9%
Z 1368
 
3.9%
K 1363
 
3.9%
F 1363
 
3.9%
Other values (16) 21380
60.7%
Decimal Number
ValueCountFrequency (%)
2 2901
10.3%
9 2881
10.2%
6 2870
10.2%
7 2836
10.1%
0 2831
10.0%
8 2812
10.0%
5 2810
10.0%
3 2791
9.9%
1 2726
9.7%
4 2714
9.6%
Dash Punctuation
ValueCountFrequency (%)
- 7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35215
50.0%
Latin 35215
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1442
 
4.1%
H 1396
 
4.0%
B 1393
 
4.0%
S 1386
 
3.9%
V 1382
 
3.9%
T 1374
 
3.9%
C 1368
 
3.9%
Z 1368
 
3.9%
K 1363
 
3.9%
F 1363
 
3.9%
Other values (16) 21380
60.7%
Common
ValueCountFrequency (%)
- 7043
20.0%
2 2901
8.2%
9 2881
8.2%
6 2870
8.1%
7 2836
8.1%
0 2831
8.0%
8 2812
 
8.0%
5 2810
 
8.0%
3 2791
 
7.9%
1 2726
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Male
3555 
Female
3488 

Length

Max length6
Median length4
Mean length4.990487
Min length4

Characters and Unicode

Total characters35148
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 3555
50.5%
Female 3488
49.5%

Length

2025-02-25T15:38:18.577649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:18.709485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 3555
50.5%
female 3488
49.5%

Most occurring characters

ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28105
80.0%
Uppercase Letter 7043
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10531
37.5%
a 7043
25.1%
l 7043
25.1%
m 3488
 
12.4%
Uppercase Letter
ValueCountFrequency (%)
M 3555
50.5%
F 3488
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 35148
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

age
Real number (ℝ)

Distinct62
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.509726
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:18.883891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q132
median46
Q360
95-th percentile75
Maximum80
Range61
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.750352
Coefficient of variation (CV)0.36014729
Kurtosis-1.0028495
Mean46.509726
Median Absolute Deviation (MAD)14
Skewness0.16218645
Sum327568
Variance280.57428
MonotonicityNot monotonic
2025-02-25T15:38:19.130009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 156
 
2.2%
47 153
 
2.2%
40 150
 
2.1%
44 148
 
2.1%
23 146
 
2.1%
56 144
 
2.0%
62 143
 
2.0%
35 142
 
2.0%
21 140
 
2.0%
33 139
 
2.0%
Other values (52) 5582
79.3%
ValueCountFrequency (%)
19 127
1.8%
20 127
1.8%
21 140
2.0%
22 130
1.8%
23 146
2.1%
24 109
1.5%
25 138
2.0%
26 115
1.6%
27 132
1.9%
28 119
1.7%
ValueCountFrequency (%)
80 66
0.9%
79 76
1.1%
78 63
0.9%
77 72
1.0%
76 69
1.0%
75 74
1.1%
74 76
1.1%
73 85
1.2%
72 58
0.8%
71 68
1.0%

married
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3641 
True
3402 
ValueCountFrequency (%)
False 3641
51.7%
True 3402
48.3%
2025-02-25T15:38:19.286798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

number_of_dependents
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46869232
Minimum0
Maximum9
Zeros5416
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:19.396988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96280195
Coefficient of variation (CV)2.0542303
Kurtosis4.4463579
Mean0.46869232
Median Absolute Deviation (MAD)0
Skewness2.109932
Sum3301
Variance0.9269876
MonotonicityNot monotonic
2025-02-25T15:38:19.532633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
5 10
 
0.1%
4 9
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
4 9
 
0.1%
5 10
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 3
 
< 0.1%
5 10
 
0.1%
4 9
 
0.1%
3 517
 
7.3%
2 531
 
7.5%
1 553
 
7.9%
0 5416
76.9%

city
Text

Distinct1106
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:19.945290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length19
Mean length9.2034644
Min length3

Characters and Unicode

Total characters64820
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrazier Park
2nd rowGlendale
3rd rowCosta Mesa
4th rowMartinez
5th rowCamarillo
ValueCountFrequency (%)
san 718
 
6.9%
los 337
 
3.3%
angeles 293
 
2.8%
diego 285
 
2.8%
santa 181
 
1.8%
valley 171
 
1.7%
beach 169
 
1.6%
city 150
 
1.5%
sacramento 116
 
1.1%
jose 112
 
1.1%
Other values (1110) 7807
75.5%
2025-02-25T15:38:20.587427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51185
79.0%
Uppercase Letter 10339
 
16.0%
Space Separator 3296
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6946
13.6%
e 6111
11.9%
n 5134
10.0%
o 5074
9.9%
l 3970
7.8%
r 3568
 
7.0%
i 3423
 
6.7%
s 2853
 
5.6%
t 2602
 
5.1%
d 1669
 
3.3%
Other values (16) 9835
19.2%
Uppercase Letter
ValueCountFrequency (%)
S 1576
15.2%
C 977
 
9.4%
L 869
 
8.4%
B 731
 
7.1%
A 651
 
6.3%
M 599
 
5.8%
P 582
 
5.6%
D 533
 
5.2%
F 471
 
4.6%
R 447
 
4.3%
Other values (15) 2903
28.1%
Space Separator
ValueCountFrequency (%)
3296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61524
94.9%
Common 3296
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6946
 
11.3%
e 6111
 
9.9%
n 5134
 
8.3%
o 5074
 
8.2%
l 3970
 
6.5%
r 3568
 
5.8%
i 3423
 
5.6%
s 2853
 
4.6%
t 2602
 
4.2%
d 1669
 
2.7%
Other values (41) 20174
32.8%
Common
ValueCountFrequency (%)
3296
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

zip_code
Real number (ℝ)

High correlation 

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93486.071
Minimum90001
Maximum96150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:20.762985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90241.1
Q192101
median93518
Q395329
95-th percentile96020.9
Maximum96150
Range6149
Interquartile range (IQR)3228

Descriptive statistics

Standard deviation1856.7675
Coefficient of variation (CV)0.019861435
Kurtosis-1.1739154
Mean93486.071
Median Absolute Deviation (MAD)1605
Skewness-0.20961512
Sum6.584224 × 108
Variance3447585.6
MonotonicityNot monotonic
2025-02-25T15:38:20.980503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92028 43
 
0.6%
92027 38
 
0.5%
92122 36
 
0.5%
92117 34
 
0.5%
92126 32
 
0.5%
92592 30
 
0.4%
92109 27
 
0.4%
92130 22
 
0.3%
92121 20
 
0.3%
92129 16
 
0.2%
Other values (1616) 6745
95.8%
ValueCountFrequency (%)
90001 4
0.1%
90002 4
0.1%
90003 5
0.1%
90004 5
0.1%
90005 4
0.1%
90006 5
0.1%
90007 5
0.1%
90008 5
0.1%
90010 4
0.1%
90011 5
0.1%
ValueCountFrequency (%)
96150 2
< 0.1%
96148 4
0.1%
96146 4
0.1%
96145 3
< 0.1%
96143 4
0.1%
96142 3
< 0.1%
96141 3
< 0.1%
96140 4
0.1%
96137 4
0.1%
96136 4
0.1%

latitude
Real number (ℝ)

High correlation 

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.197455
Minimum32.555828
Maximum41.962127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:21.194744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32.555828
5-th percentile32.886925
Q133.990646
median36.205465
Q338.161321
95-th percentile40.497425
Maximum41.962127
Range9.406299
Interquartile range (IQR)4.170675

Descriptive statistics

Standard deviation2.4689287
Coefficient of variation (CV)0.068207245
Kurtosis-1.1605061
Mean36.197455
Median Absolute Deviation (MAD)2.169863
Skewness0.31480427
Sum254938.67
Variance6.0956088
MonotonicityNot monotonic
2025-02-25T15:38:21.419292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.362575 43
 
0.6%
33.141265 38
 
0.5%
32.85723 36
 
0.5%
32.825086 34
 
0.5%
32.886925 32
 
0.5%
33.507255 30
 
0.4%
32.787836 27
 
0.4%
32.957195 22
 
0.3%
32.898613 20
 
0.3%
32.961064 16
 
0.2%
Other values (1616) 6745
95.8%
ValueCountFrequency (%)
32.555828 5
0.1%
32.578103 4
0.1%
32.579134 4
0.1%
32.587557 5
0.1%
32.605012 4
0.1%
32.607964 5
0.1%
32.619465 5
0.1%
32.622999 4
0.1%
32.636792 4
0.1%
32.64164 5
0.1%
ValueCountFrequency (%)
41.962127 4
0.1%
41.950683 4
0.1%
41.949216 4
0.1%
41.932207 3
< 0.1%
41.924174 3
< 0.1%
41.867908 4
0.1%
41.831901 4
0.1%
41.816595 4
0.1%
41.813521 4
0.1%
41.769709 4
0.1%

longitude
Real number (ℝ)

High correlation 

Distinct1625
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.75668
Minimum-124.30137
Maximum-114.1929
Zeros0
Zeros (%)0.0%
Negative7043
Negative (%)100.0%
Memory size55.2 KiB
2025-02-25T15:38:21.634042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-124.30137
5-th percentile-122.9755
Q1-121.78809
median-119.59529
Q3-117.9698
95-th percentile-116.87326
Maximum-114.1929
Range10.108471
Interquartile range (IQR)3.818295

Descriptive statistics

Standard deviation2.1544251
Coefficient of variation (CV)-0.01799002
Kurtosis-1.1912906
Mean-119.75668
Median Absolute Deviation (MAD)1.848851
Skewness-0.091931635
Sum-843446.32
Variance4.6415475
MonotonicityNot monotonic
2025-02-25T15:38:21.862592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-117.299644 43
 
0.6%
-116.967221 38
 
0.5%
-117.209774 36
 
0.5%
-117.199424 34
 
0.5%
-117.152162 32
 
0.5%
-117.029473 30
 
0.4%
-117.232376 27
 
0.4%
-117.202542 22
 
0.3%
-117.202937 20
 
0.3%
-117.134917 16
 
0.2%
Other values (1615) 6745
95.8%
ValueCountFrequency (%)
-124.301372 4
0.1%
-124.240051 4
0.1%
-124.217378 4
0.1%
-124.210902 4
0.1%
-124.189977 4
0.1%
-124.163234 4
0.1%
-124.15428 4
0.1%
-124.121504 4
0.1%
-124.108897 4
0.1%
-124.098739 4
0.1%
ValueCountFrequency (%)
-114.192901 4
0.1%
-114.36514 5
0.1%
-114.702256 4
0.1%
-114.71612 4
0.1%
-114.758334 5
0.1%
-114.850784 4
0.1%
-115.152865 2
 
< 0.1%
-115.191857 5
0.1%
-115.257009 5
0.1%
-115.287901 4
0.1%

number_of_referrals
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9518671
Minimum0
Maximum11
Zeros3821
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:22.034146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0011993
Coefficient of variation (CV)1.5376043
Kurtosis0.72196393
Mean1.9518671
Median Absolute Deviation (MAD)0
Skewness1.4460596
Sum13747
Variance9.0071972
MonotonicityNot monotonic
2025-02-25T15:38:22.172505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
5 264
 
3.7%
3 255
 
3.6%
7 248
 
3.5%
9 238
 
3.4%
2 236
 
3.4%
4 236
 
3.4%
10 223
 
3.2%
6 221
 
3.1%
Other values (2) 215
 
3.1%
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
2 236
 
3.4%
3 255
 
3.6%
4 236
 
3.4%
5 264
 
3.7%
6 221
 
3.1%
7 248
 
3.5%
8 213
 
3.0%
9 238
 
3.4%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 223
3.2%
9 238
3.4%
8 213
3.0%
7 248
3.5%
6 221
3.1%
5 264
3.7%
4 236
3.4%
3 255
3.6%
2 236
3.4%

tenure_in_months
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.386767
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:22.351000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.542061
Coefficient of variation (CV)0.75778052
Kurtosis-1.3870524
Mean32.386767
Median Absolute Deviation (MAD)22
Skewness0.24054261
Sum228100
Variance602.31276
MonotonicityNot monotonic
2025-02-25T15:38:22.564028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 613
 
8.7%
72 362
 
5.1%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
71 170
 
2.4%
5 133
 
1.9%
7 131
 
1.9%
10 127
 
1.8%
8 123
 
1.7%
Other values (62) 4770
67.7%
ValueCountFrequency (%)
1 613
8.7%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
5 133
 
1.9%
6 110
 
1.6%
7 131
 
1.9%
8 123
 
1.7%
9 119
 
1.7%
10 127
 
1.8%
ValueCountFrequency (%)
72 362
5.1%
71 170
2.4%
70 119
 
1.7%
69 95
 
1.3%
68 100
 
1.4%
67 98
 
1.4%
66 89
 
1.3%
65 76
 
1.1%
64 80
 
1.1%
63 72
 
1.0%

offer
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
not specified
3877 
offer b
824 
offer e
805 
offer d
602 
offer a
520 

Length

Max length13
Median length13
Mean length10.302854
Min length7

Characters and Unicode

Total characters72563
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot specified
2nd rownot specified
3rd rowoffer e
4th rowoffer d
5th rownot specified

Common Values

ValueCountFrequency (%)
not specified 3877
55.0%
offer b 824
 
11.7%
offer e 805
 
11.4%
offer d 602
 
8.5%
offer a 520
 
7.4%
offer c 415
 
5.9%

Length

2025-02-25T15:38:22.753535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:22.886271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
not 3877
27.5%
specified 3877
27.5%
offer 3166
22.5%
b 824
 
5.8%
e 805
 
5.7%
d 602
 
4.3%
a 520
 
3.7%
c 415
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 11725
16.2%
f 10209
14.1%
i 7754
10.7%
o 7043
9.7%
7043
9.7%
d 4479
 
6.2%
c 4292
 
5.9%
s 3877
 
5.3%
t 3877
 
5.3%
n 3877
 
5.3%
Other values (4) 8387
11.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65520
90.3%
Space Separator 7043
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11725
17.9%
f 10209
15.6%
i 7754
11.8%
o 7043
10.7%
d 4479
 
6.8%
c 4292
 
6.6%
s 3877
 
5.9%
t 3877
 
5.9%
n 3877
 
5.9%
p 3877
 
5.9%
Other values (3) 4510
 
6.9%
Space Separator
ValueCountFrequency (%)
7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65520
90.3%
Common 7043
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11725
17.9%
f 10209
15.6%
i 7754
11.8%
o 7043
10.7%
d 4479
 
6.8%
c 4292
 
6.6%
s 3877
 
5.9%
t 3877
 
5.9%
n 3877
 
5.9%
p 3877
 
5.9%
Other values (3) 4510
 
6.9%
Common
ValueCountFrequency (%)
7043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11725
16.2%
f 10209
14.1%
i 7754
10.7%
o 7043
9.7%
7043
9.7%
d 4479
 
6.2%
c 4292
 
5.9%
s 3877
 
5.3%
t 3877
 
5.3%
n 3877
 
5.3%
Other values (4) 8387
11.6%

phone_service
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True 6361
90.3%
False 682
 
9.7%
2025-02-25T15:38:23.023109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

avg_monthly_long_distance_charges
Real number (ℝ)

High correlation  Zeros 

Distinct3584
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.958954
Minimum0
Maximum49.99
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:23.175180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.21
median22.89
Q336.395
95-th percentile47.34
Maximum49.99
Range49.99
Interquartile range (IQR)27.185

Descriptive statistics

Standard deviation15.448113
Coefficient of variation (CV)0.6728579
Kurtosis-1.2546544
Mean22.958954
Median Absolute Deviation (MAD)13.6
Skewness0.049175899
Sum161699.91
Variance238.64421
MonotonicityNot monotonic
2025-02-25T15:38:23.405536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
9.7%
18.26 7
 
0.1%
22.83 6
 
0.1%
45.92 6
 
0.1%
30.07 6
 
0.1%
18.74 6
 
0.1%
41.93 6
 
0.1%
49.51 6
 
0.1%
42.55 6
 
0.1%
27.97 6
 
0.1%
Other values (3574) 6306
89.5%
ValueCountFrequency (%)
0 682
9.7%
1.01 1
 
< 0.1%
1.02 3
 
< 0.1%
1.03 1
 
< 0.1%
1.05 1
 
< 0.1%
1.06 1
 
< 0.1%
1.07 1
 
< 0.1%
1.08 2
 
< 0.1%
1.09 2
 
< 0.1%
1.1 1
 
< 0.1%
ValueCountFrequency (%)
49.99 1
 
< 0.1%
49.98 3
< 0.1%
49.96 2
< 0.1%
49.95 2
< 0.1%
49.94 1
 
< 0.1%
49.92 1
 
< 0.1%
49.91 3
< 0.1%
49.9 3
< 0.1%
49.88 1
 
< 0.1%
49.87 1
 
< 0.1%

multiple_lines
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4072 
True
2971 
ValueCountFrequency (%)
False 4072
57.8%
True 2971
42.2%
2025-02-25T15:38:23.569273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

internet_service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
5517 
False
1526 
ValueCountFrequency (%)
True 5517
78.3%
False 1526
 
21.7%
2025-02-25T15:38:23.661219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

internet_type
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
fiber optic
3035 
dsl
1652 
not specified
1526 
cable
830 

Length

Max length13
Median length11
Mean length8.8497799
Min length3

Characters and Unicode

Total characters62329
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcable
2nd rowcable
3rd rowfiber optic
4th rowfiber optic
5th rowfiber optic

Common Values

ValueCountFrequency (%)
fiber optic 3035
43.1%
dsl 1652
23.5%
not specified 1526
21.7%
cable 830
 
11.8%

Length

2025-02-25T15:38:23.799388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:23.928801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fiber 3035
26.2%
optic 3035
26.2%
dsl 1652
14.2%
not 1526
13.2%
specified 1526
13.2%
cable 830
 
7.2%

Most occurring characters

ValueCountFrequency (%)
i 9122
14.6%
e 6917
11.1%
c 5391
8.6%
o 4561
7.3%
4561
7.3%
p 4561
7.3%
f 4561
7.3%
t 4561
7.3%
b 3865
 
6.2%
s 3178
 
5.1%
Other values (5) 11051
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57768
92.7%
Space Separator 4561
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9122
15.8%
e 6917
12.0%
c 5391
9.3%
o 4561
7.9%
p 4561
7.9%
f 4561
7.9%
t 4561
7.9%
b 3865
6.7%
s 3178
 
5.5%
d 3178
 
5.5%
Other values (4) 7873
13.6%
Space Separator
ValueCountFrequency (%)
4561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57768
92.7%
Common 4561
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9122
15.8%
e 6917
12.0%
c 5391
9.3%
o 4561
7.9%
p 4561
7.9%
f 4561
7.9%
t 4561
7.9%
b 3865
6.7%
s 3178
 
5.5%
d 3178
 
5.5%
Other values (4) 7873
13.6%
Common
ValueCountFrequency (%)
4561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9122
14.6%
e 6917
11.1%
c 5391
8.6%
o 4561
7.3%
4561
7.3%
p 4561
7.3%
f 4561
7.3%
t 4561
7.3%
b 3865
 
6.2%
s 3178
 
5.1%
Other values (5) 11051
17.7%

avg_monthly_gb_download
Real number (ℝ)

High correlation  Missing 

Distinct49
Distinct (%)0.9%
Missing1526
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean26.189958
Minimum2
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:24.127984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q113
median21
Q330
95-th percentile71
Maximum85
Range83
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.586585
Coefficient of variation (CV)0.74786623
Kurtosis0.63684154
Mean26.189958
Median Absolute Deviation (MAD)9
Skewness1.184056
Sum144490
Variance383.63432
MonotonicityNot monotonic
2025-02-25T15:38:24.328480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
19 220
 
3.1%
27 199
 
2.8%
30 193
 
2.7%
59 192
 
2.7%
26 191
 
2.7%
23 179
 
2.5%
22 172
 
2.4%
21 171
 
2.4%
18 164
 
2.3%
13 164
 
2.3%
Other values (39) 3672
52.1%
(Missing) 1526
21.7%
ValueCountFrequency (%)
2 116
1.6%
3 130
1.8%
4 129
1.8%
5 114
1.6%
6 114
1.6%
7 116
1.6%
8 120
1.7%
9 116
1.6%
10 132
1.9%
11 145
2.1%
ValueCountFrequency (%)
85 48
 
0.7%
82 43
 
0.6%
76 58
 
0.8%
75 15
 
0.2%
73 81
1.2%
71 42
 
0.6%
69 75
 
1.1%
59 192
2.7%
58 45
 
0.6%
57 34
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5024 
True
2019 
ValueCountFrequency (%)
False 5024
71.3%
True 2019
28.7%
2025-02-25T15:38:24.456338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

online_backup
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4614 
True
2429 
ValueCountFrequency (%)
False 4614
65.5%
True 2429
34.5%
2025-02-25T15:38:24.530005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

device_protection_plan
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4621 
True
2422 
ValueCountFrequency (%)
False 4621
65.6%
True 2422
34.4%
2025-02-25T15:38:24.613231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4999 
True
2044 
ValueCountFrequency (%)
False 4999
71.0%
True 2044
29.0%
2025-02-25T15:38:24.690989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

streaming_tv
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4336 
True
2707 
ValueCountFrequency (%)
False 4336
61.6%
True 2707
38.4%
2025-02-25T15:38:24.766673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

streaming_movies
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4311 
True
2732 
ValueCountFrequency (%)
False 4311
61.2%
True 2732
38.8%
2025-02-25T15:38:24.843482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

streaming_music
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4555 
True
2488 
ValueCountFrequency (%)
False 4555
64.7%
True 2488
35.3%
2025-02-25T15:38:24.923568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

unlimited_data
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4745 
False
2298 
ValueCountFrequency (%)
True 4745
67.4%
False 2298
32.6%
2025-02-25T15:38:24.999239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

contract
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Month-to-Month
3610 
Two Year
1883 
One Year
1550 

Length

Max length14
Median length14
Mean length11.075394
Min length8

Characters and Unicode

Total characters78004
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOne Year
2nd rowMonth-to-Month
3rd rowMonth-to-Month
4th rowMonth-to-Month
5th rowMonth-to-Month

Common Values

ValueCountFrequency (%)
Month-to-Month 3610
51.3%
Two Year 1883
26.7%
One Year 1550
22.0%

Length

2025-02-25T15:38:25.142423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:25.261170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month 3610
34.5%
year 3433
32.8%
two 1883
18.0%
one 1550
14.8%

Most occurring characters

ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
Y 3433
 
4.4%
a 3433
 
4.4%
3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53265
68.3%
Uppercase Letter 14086
 
18.1%
Dash Punctuation 7220
 
9.3%
Space Separator 3433
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 12713
23.9%
t 10830
20.3%
n 8770
16.5%
h 7220
13.6%
e 4983
 
9.4%
a 3433
 
6.4%
r 3433
 
6.4%
w 1883
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
M 7220
51.3%
Y 3433
24.4%
T 1883
 
13.4%
O 1550
 
11.0%
Dash Punctuation
ValueCountFrequency (%)
- 7220
100.0%
Space Separator
ValueCountFrequency (%)
3433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67351
86.3%
Common 10653
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 12713
18.9%
t 10830
16.1%
n 8770
13.0%
M 7220
10.7%
h 7220
10.7%
e 4983
 
7.4%
Y 3433
 
5.1%
a 3433
 
5.1%
r 3433
 
5.1%
w 1883
 
2.8%
Other values (2) 3433
 
5.1%
Common
ValueCountFrequency (%)
- 7220
67.8%
3433
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
Y 3433
 
4.4%
a 3433
 
4.4%
3433
 
4.4%
Other values (4) 8749
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True 4171
59.2%
False 2872
40.8%
2025-02-25T15:38:25.360735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

payment_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
bank withdrawal
3909 
credit card
2749 
mailed check
 
385

Length

Max length15
Median length15
Mean length13.274741
Min length11

Characters and Unicode

Total characters93494
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit card
2nd rowcredit card
3rd rowbank withdrawal
4th rowbank withdrawal
5th rowcredit card

Common Values

ValueCountFrequency (%)
bank withdrawal 3909
55.5%
credit card 2749
39.0%
mailed check 385
 
5.5%

Length

2025-02-25T15:38:25.511600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:25.661516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bank 3909
27.8%
withdrawal 3909
27.8%
credit 2749
19.5%
card 2749
19.5%
mailed 385
 
2.7%
check 385
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
w 7818
8.4%
7043
7.5%
i 7043
7.5%
t 6658
7.1%
c 6268
 
6.7%
h 4294
 
4.6%
k 4294
 
4.6%
Other values (5) 16016
17.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 86451
92.5%
Space Separator 7043
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14861
17.2%
d 9792
11.3%
r 9407
10.9%
w 7818
9.0%
i 7043
8.1%
t 6658
7.7%
c 6268
7.3%
h 4294
 
5.0%
k 4294
 
5.0%
l 4294
 
5.0%
Other values (4) 11722
13.6%
Space Separator
ValueCountFrequency (%)
7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86451
92.5%
Common 7043
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14861
17.2%
d 9792
11.3%
r 9407
10.9%
w 7818
9.0%
i 7043
8.1%
t 6658
7.7%
c 6268
7.3%
h 4294
 
5.0%
k 4294
 
5.0%
l 4294
 
5.0%
Other values (4) 11722
13.6%
Common
ValueCountFrequency (%)
7043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
w 7818
8.4%
7043
7.5%
i 7043
7.5%
t 6658
7.1%
c 6268
 
6.7%
h 4294
 
4.6%
k 4294
 
4.6%
Other values (5) 16016
17.1%

monthly_charge
Real number (ℝ)

High correlation  Zeros 

Distinct1582
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.688421
Minimum0
Maximum118.75
Zeros120
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:25.855295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.5
Q130.4
median70.05
Q389.75
95-th percentile107.195
Maximum118.75
Range118.75
Interquartile range (IQR)59.35

Descriptive statistics

Standard deviation31.005583
Coefficient of variation (CV)0.48683234
Kurtosis-1.1953569
Mean63.688421
Median Absolute Deviation (MAD)24.5
Skewness-0.24781026
Sum448557.55
Variance961.34619
MonotonicityNot monotonic
2025-02-25T15:38:26.106932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
1.7%
20.05 60
 
0.9%
19.85 45
 
0.6%
19.95 44
 
0.6%
19.9 44
 
0.6%
20 43
 
0.6%
19.65 42
 
0.6%
19.7 40
 
0.6%
20.25 39
 
0.6%
19.55 39
 
0.6%
Other values (1572) 6527
92.7%
ValueCountFrequency (%)
0 120
1.7%
18.25 1
 
< 0.1%
18.4 1
 
< 0.1%
18.55 1
 
< 0.1%
18.7 2
 
< 0.1%
18.75 1
 
< 0.1%
18.8 7
 
0.1%
18.85 5
 
0.1%
18.9 2
 
< 0.1%
18.95 6
 
0.1%
ValueCountFrequency (%)
118.75 1
< 0.1%
118.65 1
< 0.1%
118.6 2
< 0.1%
118.35 1
< 0.1%
118.2 1
< 0.1%
117.8 1
< 0.1%
117.6 1
< 0.1%
117.5 1
< 0.1%
117.45 1
< 0.1%
117.35 1
< 0.1%

total_charges
Real number (ℝ)

High correlation 

Distinct6540
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2280.3813
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:26.356043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.65
Q1400.15
median1394.55
Q33786.6
95-th percentile6921.025
Maximum8684.8
Range8666
Interquartile range (IQR)3386.45

Descriptive statistics

Standard deviation2266.2205
Coefficient of variation (CV)0.99379016
Kurtosis-0.22769266
Mean2280.3813
Median Absolute Deviation (MAD)1219.75
Skewness0.96379109
Sum16060725
Variance5135755.2
MonotonicityNot monotonic
2025-02-25T15:38:26.607998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.2 11
 
0.2%
19.75 9
 
0.1%
19.9 8
 
0.1%
19.65 8
 
0.1%
20.05 8
 
0.1%
45.3 7
 
0.1%
19.55 7
 
0.1%
20.25 6
 
0.1%
19.45 6
 
0.1%
20.15 6
 
0.1%
Other values (6530) 6967
98.9%
ValueCountFrequency (%)
18.8 1
 
< 0.1%
18.85 2
< 0.1%
18.9 1
 
< 0.1%
19 1
 
< 0.1%
19.05 1
 
< 0.1%
19.1 3
< 0.1%
19.15 1
 
< 0.1%
19.2 4
0.1%
19.25 3
< 0.1%
19.3 4
0.1%
ValueCountFrequency (%)
8684.8 1
< 0.1%
8672.45 1
< 0.1%
8670.1 1
< 0.1%
8594.4 1
< 0.1%
8564.75 1
< 0.1%
8547.15 1
< 0.1%
8543.25 1
< 0.1%
8529.5 1
< 0.1%
8496.7 1
< 0.1%
8477.7 1
< 0.1%

total_refunds
Real number (ℝ)

Zeros 

Distinct500
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9621823
Minimum0
Maximum49.79
Zeros6518
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:26.857244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18.149
Maximum49.79
Range49.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9026144
Coefficient of variation (CV)4.0274618
Kurtosis18.350658
Mean1.9621823
Median Absolute Deviation (MAD)0
Skewness4.3285167
Sum13819.65
Variance62.451314
MonotonicityNot monotonic
2025-02-25T15:38:27.113861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6518
92.5%
20.45 2
 
< 0.1%
16.56 2
 
< 0.1%
47.04 2
 
< 0.1%
5.73 2
 
< 0.1%
14.23 2
 
< 0.1%
18.55 2
 
< 0.1%
8.74 2
 
< 0.1%
49.57 2
 
< 0.1%
1.31 2
 
< 0.1%
Other values (490) 507
 
7.2%
ValueCountFrequency (%)
0 6518
92.5%
1.01 1
 
< 0.1%
1.09 1
 
< 0.1%
1.27 1
 
< 0.1%
1.31 2
 
< 0.1%
1.48 1
 
< 0.1%
1.65 1
 
< 0.1%
1.66 1
 
< 0.1%
1.69 1
 
< 0.1%
1.83 1
 
< 0.1%
ValueCountFrequency (%)
49.79 1
< 0.1%
49.76 1
< 0.1%
49.57 2
< 0.1%
49.53 1
< 0.1%
49.51 1
< 0.1%
49.38 1
< 0.1%
49.37 1
< 0.1%
49.24 1
< 0.1%
49.23 1
< 0.1%
49.22 1
< 0.1%

total_extra_data_charges
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8607128
Minimum0
Maximum150
Zeros6315
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:27.312929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile60
Maximum150
Range150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.104978
Coefficient of variation (CV)3.6592376
Kurtosis16.458874
Mean6.8607128
Median Absolute Deviation (MAD)0
Skewness4.0912092
Sum48320
Variance630.25992
MonotonicityNot monotonic
2025-02-25T15:38:27.469107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6315
89.7%
10 138
 
2.0%
40 62
 
0.9%
30 58
 
0.8%
20 51
 
0.7%
80 47
 
0.7%
100 44
 
0.6%
50 43
 
0.6%
150 42
 
0.6%
130 40
 
0.6%
Other values (6) 203
 
2.9%
ValueCountFrequency (%)
0 6315
89.7%
10 138
 
2.0%
20 51
 
0.7%
30 58
 
0.8%
40 62
 
0.9%
50 43
 
0.6%
60 36
 
0.5%
70 34
 
0.5%
80 47
 
0.7%
90 35
 
0.5%
ValueCountFrequency (%)
150 42
0.6%
140 38
0.5%
130 40
0.6%
120 28
0.4%
110 32
0.5%
100 44
0.6%
90 35
0.5%
80 47
0.7%
70 34
0.5%
60 36
0.5%

total_long_distance_charges
Real number (ℝ)

High correlation  Zeros 

Distinct6068
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.09926
Minimum0
Maximum3564.72
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:27.688971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.545
median401.44
Q31191.1
95-th percentile2577.877
Maximum3564.72
Range3564.72
Interquartile range (IQR)1120.555

Descriptive statistics

Standard deviation846.66005
Coefficient of variation (CV)1.1302375
Kurtosis0.64409208
Mean749.09926
Median Absolute Deviation (MAD)382.12
Skewness1.238282
Sum5275906.1
Variance716833.25
MonotonicityNot monotonic
2025-02-25T15:38:27.917360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
9.7%
48.96 4
 
0.1%
22.86 4
 
0.1%
15.6 4
 
0.1%
808.08 3
 
< 0.1%
26 3
 
< 0.1%
2077.92 3
 
< 0.1%
15.28 3
 
< 0.1%
24.48 3
 
< 0.1%
200.34 3
 
< 0.1%
Other values (6058) 6331
89.9%
ValueCountFrequency (%)
0 682
9.7%
1.13 1
 
< 0.1%
1.15 1
 
< 0.1%
1.17 1
 
< 0.1%
1.23 1
 
< 0.1%
1.28 1
 
< 0.1%
1.47 1
 
< 0.1%
1.48 1
 
< 0.1%
1.5 1
 
< 0.1%
1.59 1
 
< 0.1%
ValueCountFrequency (%)
3564.72 1
< 0.1%
3564 1
< 0.1%
3536.64 1
< 0.1%
3515.92 1
< 0.1%
3508.82 1
< 0.1%
3501.72 1
< 0.1%
3493.44 1
< 0.1%
3492.72 1
< 0.1%
3487.68 1
< 0.1%
3482.64 1
< 0.1%

total_revenue
Real number (ℝ)

High correlation 

Distinct6975
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3034.3791
Minimum21.36
Maximum11979.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-02-25T15:38:28.171559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21.36
5-th percentile78.452
Q1605.61
median2108.64
Q34801.145
95-th percentile8747.041
Maximum11979.34
Range11957.98
Interquartile range (IQR)4195.535

Descriptive statistics

Standard deviation2865.2045
Coefficient of variation (CV)0.9442474
Kurtosis-0.20345739
Mean3034.3791
Median Absolute Deviation (MAD)1767.61
Skewness0.91941027
Sum21371132
Variance8209397.1
MonotonicityNot monotonic
2025-02-25T15:38:28.683775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.56 3
 
< 0.1%
68.41 3
 
< 0.1%
24.8 3
 
< 0.1%
116.27 3
 
< 0.1%
90.55 2
 
< 0.1%
60.18 2
 
< 0.1%
105.89 2
 
< 0.1%
55.14 2
 
< 0.1%
622.45 2
 
< 0.1%
226.45 2
 
< 0.1%
Other values (6965) 7019
99.7%
ValueCountFrequency (%)
21.36 1
< 0.1%
21.4 1
< 0.1%
21.61 1
< 0.1%
22.08 1
< 0.1%
22.12 1
< 0.1%
22.25 1
< 0.1%
22.28 1
< 0.1%
22.54 1
< 0.1%
23.24 2
< 0.1%
23.28 1
< 0.1%
ValueCountFrequency (%)
11979.34 1
< 0.1%
11868.34 1
< 0.1%
11795.78 1
< 0.1%
11688.9 1
< 0.1%
11634.53 1
< 0.1%
11596.99 1
< 0.1%
11564.37 1
< 0.1%
11529.54 1
< 0.1%
11514.81 1
< 0.1%
11501.82 1
< 0.1%

customer_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
stayed
4720 
churned
1869 
joined
 
454

Length

Max length7
Median length6
Mean length6.2653699
Min length6

Characters and Unicode

Total characters44127
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstayed
2nd rowstayed
3rd rowchurned
4th rowchurned
5th rowchurned

Common Values

ValueCountFrequency (%)
stayed 4720
67.0%
churned 1869
 
26.5%
joined 454
 
6.4%

Length

2025-02-25T15:38:28.891291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:29.009039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
stayed 4720
67.0%
churned 1869
 
26.5%
joined 454
 
6.4%

Most occurring characters

ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
t 4720
10.7%
s 4720
10.7%
y 4720
10.7%
a 4720
10.7%
n 2323
 
5.3%
c 1869
 
4.2%
u 1869
 
4.2%
h 1869
 
4.2%
Other values (4) 3231
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44127
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
t 4720
10.7%
s 4720
10.7%
y 4720
10.7%
a 4720
10.7%
n 2323
 
5.3%
c 1869
 
4.2%
u 1869
 
4.2%
h 1869
 
4.2%
Other values (4) 3231
7.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 44127
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
t 4720
10.7%
s 4720
10.7%
y 4720
10.7%
a 4720
10.7%
n 2323
 
5.3%
c 1869
 
4.2%
u 1869
 
4.2%
h 1869
 
4.2%
Other values (4) 3231
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
t 4720
10.7%
s 4720
10.7%
y 4720
10.7%
a 4720
10.7%
n 2323
 
5.3%
c 1869
 
4.2%
u 1869
 
4.2%
h 1869
 
4.2%
Other values (4) 3231
7.3%

churn_category
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
not specified
5174 
Competitor
841 
Dissatisfaction
 
321
Attitude
 
314
Price
 
211

Length

Max length15
Median length13
Mean length12.063609
Min length5

Characters and Unicode

Total characters84964
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot specified
2nd rownot specified
3rd rowCompetitor
4th rowDissatisfaction
5th rowDissatisfaction

Common Values

ValueCountFrequency (%)
not specified 5174
73.5%
Competitor 841
 
11.9%
Dissatisfaction 321
 
4.6%
Attitude 314
 
4.5%
Price 211
 
3.0%
Other 182
 
2.6%

Length

2025-02-25T15:38:29.175231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:29.304964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
not 5174
42.4%
specified 5174
42.4%
competitor 841
 
6.9%
dissatisfaction 321
 
2.6%
attitude 314
 
2.6%
price 211
 
1.7%
other 182
 
1.5%

Most occurring characters

ValueCountFrequency (%)
i 12677
14.9%
e 11896
14.0%
t 8622
10.1%
o 7177
8.4%
s 6137
7.2%
p 6015
7.1%
c 5706
6.7%
f 5495
6.5%
n 5495
6.5%
d 5488
6.5%
Other values (11) 10256
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77921
91.7%
Space Separator 5174
 
6.1%
Uppercase Letter 1869
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 12677
16.3%
e 11896
15.3%
t 8622
11.1%
o 7177
9.2%
s 6137
7.9%
p 6015
7.7%
c 5706
7.3%
f 5495
7.1%
n 5495
7.1%
d 5488
7.0%
Other values (5) 3213
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
C 841
45.0%
D 321
 
17.2%
A 314
 
16.8%
P 211
 
11.3%
O 182
 
9.7%
Space Separator
ValueCountFrequency (%)
5174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79790
93.9%
Common 5174
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 12677
15.9%
e 11896
14.9%
t 8622
10.8%
o 7177
9.0%
s 6137
7.7%
p 6015
7.5%
c 5706
7.2%
f 5495
6.9%
n 5495
6.9%
d 5488
6.9%
Other values (10) 5082
6.4%
Common
ValueCountFrequency (%)
5174
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 12677
14.9%
e 11896
14.0%
t 8622
10.1%
o 7177
8.4%
s 6137
7.2%
p 6015
7.1%
c 5706
6.7%
f 5495
6.5%
n 5495
6.5%
d 5488
6.5%
Other values (11) 10256
12.1%

churn_reason
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
not specified
5174 
Competitor Offer
841 
Dissatisfaction
 
243
attitude of support person
 
220
Other
 
219
Other values (4)
 
346

Length

Max length31
Median length13
Mean length13.829618
Min length5

Characters and Unicode

Total characters97402
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot specified
2nd rownot specified
3rd rowCompetitor Offer
4th rowDissatisfaction
5th rowDissatisfaction

Common Values

ValueCountFrequency (%)
not specified 5174
73.5%
Competitor Offer 841
 
11.9%
Dissatisfaction 243
 
3.5%
attitude of support person 220
 
3.1%
Other 219
 
3.1%
Price Issue 211
 
3.0%
attitude of service provider 94
 
1.3%
lack of self-service on website 29
 
0.4%
poor expertise of phone support 12
 
0.2%

Length

2025-02-25T15:38:29.512407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T15:38:29.669095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
not 5174
36.0%
specified 5174
36.0%
competitor 841
 
5.9%
offer 841
 
5.9%
of 355
 
2.5%
attitude 314
 
2.2%
dissatisfaction 243
 
1.7%
support 232
 
1.6%
person 220
 
1.5%
other 219
 
1.5%
Other values (11) 762
 
5.3%

Most occurring characters

ValueCountFrequency (%)
e 13680
14.0%
i 12701
13.0%
t 8776
9.0%
o 8065
8.3%
f 7483
7.7%
7332
7.5%
s 6970
7.2%
p 6829
7.0%
c 5780
5.9%
n 5678
5.8%
Other values (18) 14108
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87475
89.8%
Space Separator 7332
 
7.5%
Uppercase Letter 2566
 
2.6%
Dash Punctuation 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13680
15.6%
i 12701
14.5%
t 8776
10.0%
o 8065
9.2%
f 7483
8.6%
s 6970
8.0%
p 6829
7.8%
c 5780
6.6%
n 5678
6.5%
d 5582
6.4%
Other values (11) 5931
6.8%
Uppercase Letter
ValueCountFrequency (%)
O 1060
41.3%
C 841
32.8%
D 243
 
9.5%
P 211
 
8.2%
I 211
 
8.2%
Space Separator
ValueCountFrequency (%)
7332
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90041
92.4%
Common 7361
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13680
15.2%
i 12701
14.1%
t 8776
9.7%
o 8065
9.0%
f 7483
8.3%
s 6970
7.7%
p 6829
7.6%
c 5780
6.4%
n 5678
6.3%
d 5582
6.2%
Other values (16) 8497
9.4%
Common
ValueCountFrequency (%)
7332
99.6%
- 29
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13680
14.0%
i 12701
13.0%
t 8776
9.0%
o 8065
8.3%
f 7483
7.7%
7332
7.5%
s 6970
7.2%
p 6829
7.0%
c 5780
5.9%
n 5678
5.8%
Other values (18) 14108
14.5%

Interactions

2025-02-25T15:38:13.445871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.555804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.224079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.860973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.544581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:48.120908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:50.592956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:53.284696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:55.822227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:58.290984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:00.725351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:03.198199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:05.869014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:08.264966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:10.813840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:13.611893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.604803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.265917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.907011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.584673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:48.289133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:50.751669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:53.454235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:55.990756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:58.449852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:00.878964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:03.353033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:06.032815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:08.436702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:10.988682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:13.770643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.648548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.304913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.949711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.622672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:48.436064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:50.902774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:53.620151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:56.145931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:58.598884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:01.039717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:03.506344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:06.185591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:08.598696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:11.156414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:13.945687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.692501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.348624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.994701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.665440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:48.602626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:51.065123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:53.793254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:56.316419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:58.762758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:01.212682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:03.669694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:06.354631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:08.772415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:11.338670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:14.105525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.734592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.389223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.037706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.703364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:48.754008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:51.470059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:53.949121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:56.473582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:58.908217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:01.372961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:03.828724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:06.505116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:08.930919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:11.505914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:14.275156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.777304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.431260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.083516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.746227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:48.904779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:51.628259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:54.119026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:56.637374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:59.067070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:01.550794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:03.996050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:06.663364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:09.106683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:11.682151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:14.439111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.821259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.474869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.129497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.787145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:49.062874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:51.773818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:54.275559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:56.793884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:59.225867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:01.708756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:04.166229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:06.816801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:09.267702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:11.849651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:14.607111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.865377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.518566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.177256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.848995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:49.232083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:51.934604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:54.450503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:56.961725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:59.395189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:01.877597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:04.325740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:06.983624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:09.443179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:12.033079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:14.791920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.912462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.559948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.222724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:47.002745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:49.407677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:52.099580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:54.617456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:57.123112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:59.559220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:02.040011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:04.483662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:07.140357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:09.610153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:12.215677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:14.962802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.953158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.602018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.267284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:47.150716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:49.574240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:52.249424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:54.777648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:57.280789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:59.703626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:02.197940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:04.633588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:07.295873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:09.766154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:12.400676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:15.143611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:44.998238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.644717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.312335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:47.307466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:49.744107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:52.420952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:54.953182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:57.441365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:59.864503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:02.362688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:05.041187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:07.457308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:09.937691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:12.576535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:15.300432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.040347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.685405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.356819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:47.459740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:49.892206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:52.595892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:55.114453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:57.598372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:00.028951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:02.517928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:05.197109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:07.609292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:10.101181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:12.737418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:15.459713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.084032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.728538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.403496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:47.617682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:50.054720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:52.775590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:55.287040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:57.758205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:00.194941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:02.679312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:05.365618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:07.761042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:10.269050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:12.908944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:15.640681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.130466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.773215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.451638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:47.797120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:50.234910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:52.947245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:55.464217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:57.925654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:00.373800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:02.852768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:05.551726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:07.922607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:10.443319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:13.091779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:15.824210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.179128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:45.818245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:46.497650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:47.964712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:50.408968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:53.119166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:55.648607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:37:58.106533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:00.555843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:03.029279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:05.717232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:08.090445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:10.642248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T15:38:13.271531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-25T15:38:29.964184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageavg_monthly_gb_downloadavg_monthly_long_distance_chargeschurn_categorychurn_reasoncontractcustomer_statusdevice_protection_plangenderinternet_serviceinternet_typelatitudelongitudemarriedmonthly_chargemultiple_linesnumber_of_dependentsnumber_of_referralsofferonline_backuponline_securitypaperless_billingpayment_methodphone_servicepremium_tech_supportstreaming_moviesstreaming_musicstreaming_tvtenure_in_monthstotal_chargestotal_extra_data_chargestotal_long_distance_chargestotal_refundstotal_revenueunlimited_datazip_code
age1.000-0.498-0.0120.0670.0530.0270.1070.0450.0000.1720.139-0.0080.0040.0290.1280.139-0.120-0.0160.0360.0550.0430.1410.0990.0230.0480.1080.1800.0840.0120.0670.0310.0100.0200.0520.128-0.007
avg_monthly_gb_download-0.4981.0000.0050.0440.0360.0120.0630.0240.0001.0000.045-0.0150.0120.096-0.0230.0380.2600.0830.0000.0560.0740.0420.0080.0220.0580.0000.1650.0150.0400.0310.0060.0210.0020.0320.013-0.008
avg_monthly_long_distance_charges-0.0120.0051.0000.0180.0110.0260.0220.0430.0320.1110.1800.006-0.0050.0000.1350.207-0.0060.0080.0000.0350.0570.0000.0230.7180.0720.0000.0030.0140.0140.059-0.0090.651-0.0130.2090.0820.006
churn_category0.0670.0440.0181.0000.9730.3200.7070.0730.0000.2370.1800.0900.0690.1500.1150.0450.1090.1390.1180.0850.1710.1930.1560.0130.1640.0560.0450.0660.1620.0930.0250.1080.0000.1070.1720.077
churn_reason0.0530.0360.0110.9731.0000.3200.7060.0720.0000.2380.1800.0730.0560.1520.0910.0460.0870.1080.1170.0890.1710.1920.1570.0210.1640.0660.0460.0670.1270.0730.0200.0840.0000.0840.1710.064
contract0.0270.0120.0260.3200.3201.0000.3750.2270.0000.2020.1650.0290.0180.2810.2000.1210.1250.2210.3390.1690.2360.1500.1170.0000.2730.1250.0880.1160.4970.3460.0410.3210.0390.3600.1400.024
customer_status0.1070.0630.0220.7070.7060.3751.0000.1760.0030.2420.2240.0670.0530.2360.2120.1710.1810.2450.2820.1610.2180.1990.1610.0070.2130.1550.1320.1540.4470.3000.0380.2820.0470.3210.1820.042
device_protection_plan0.0450.0240.0430.0730.0720.2270.1761.0000.0000.3800.3800.0000.0100.1530.4990.2000.0090.1270.2460.3030.2750.1030.0790.0700.3330.4020.3490.3900.3590.5220.0760.2100.0000.4710.2960.000
gender0.0000.0000.0320.0000.0000.0000.0030.0001.0000.0000.0000.0000.0000.0000.0190.0000.0130.0000.0200.0060.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.014
internet_service0.1721.0000.1110.2370.2380.2020.2420.3800.0001.0001.0000.0600.0460.0000.9310.2100.1720.0420.0460.3810.3330.3200.2730.1710.3360.4180.3880.4150.0210.4280.1550.0450.0060.3100.7550.043
internet_type0.1390.0450.1800.1800.1800.1650.2240.3800.0001.0001.0000.0360.0370.0000.6830.3630.1090.0400.0270.3810.3880.3750.2340.4430.3860.4410.3960.4410.0120.2900.0890.0910.0000.2220.7560.027
latitude-0.008-0.0150.0060.0900.0730.0290.0670.0000.0000.0600.0361.000-0.8700.025-0.0260.0150.0290.0110.0320.0320.0200.0220.0520.0000.0000.0000.0000.0000.013-0.004-0.0020.008-0.0090.0010.0430.880
longitude0.0040.012-0.0050.0690.0560.0180.0530.0100.0000.0460.037-0.8701.0000.0210.0260.005-0.022-0.0020.0350.0000.0000.0270.0500.0000.0160.0130.0000.000-0.0120.0060.001-0.004-0.0090.0010.019-0.742
married0.0290.0960.0000.1500.1520.2810.2360.1530.0000.0000.0000.0250.0211.0000.1520.1410.3620.6820.2560.1410.1420.0080.0620.0120.1190.1170.0880.1240.3780.3240.0000.2690.0310.3330.0140.027
monthly_charge0.128-0.0230.1350.1150.0910.2000.2120.4990.0190.9310.683-0.0260.0260.1521.0000.594-0.1290.0780.1180.4590.3810.3570.2230.5590.4140.6510.5430.6550.2700.6200.1200.3070.0280.5530.704-0.006
multiple_lines0.1390.0380.2070.0450.0460.1210.1710.2000.0000.2100.3630.0150.0050.1410.5941.0000.0120.0750.2370.2020.0970.1630.1520.2790.1000.2580.1930.2570.3330.4690.0610.3330.0460.4610.1590.031
number_of_dependents-0.1200.260-0.0060.1090.0870.1250.1810.0090.0130.1720.1090.029-0.0220.362-0.1290.0121.0000.3560.0420.0000.0450.1210.0680.0290.0210.0700.0310.0540.1350.042-0.0310.0910.0180.0650.1280.017
number_of_referrals-0.0160.0830.0080.1390.1080.2210.2450.1270.0000.0420.0400.011-0.0020.6820.0780.0750.3561.0000.1060.1130.1460.0530.0540.0000.1200.0560.0530.0710.3830.327-0.0250.2570.0370.3390.0000.006
offer0.0360.0000.0000.1180.1170.3390.2820.2460.0200.0460.0270.0320.0350.2560.1180.2370.0420.1061.0000.2500.2340.0000.0680.0100.2240.1910.1610.1910.5620.3470.0290.2580.0160.3480.0320.051
online_backup0.0550.0560.0350.0850.0890.1690.1610.3030.0060.3810.3810.0320.0000.1410.4590.2020.0000.1130.2501.0000.2830.1260.0950.0500.2940.2740.2450.2820.3580.5090.1000.2430.0000.4720.2830.022
online_security0.0430.0740.0570.1710.1710.2360.2180.2750.0120.3330.3880.0200.0000.1420.3810.0970.0450.1460.2340.2831.0000.0000.0440.0920.3540.1870.1950.1750.3260.4200.0570.1980.0310.3830.2640.012
paperless_billing0.1410.0420.0000.1930.1920.1500.1990.1030.0000.3200.3750.0220.0270.0080.3570.1630.1210.0530.0000.1260.0001.0000.1850.0110.0360.2110.1660.2230.0000.1580.0430.0190.0000.1330.2450.004
payment_method0.0990.0080.0230.1560.1570.1170.1610.0790.0000.2730.2340.0520.0500.0620.2230.1520.0680.0540.0680.0950.0440.1851.0000.0240.0490.1780.1340.1810.0950.1160.0320.0720.0140.1020.1970.049
phone_service0.0230.0220.7180.0130.0210.0000.0070.0700.0000.1710.4430.0000.0000.0120.5590.2790.0290.0000.0100.0500.0920.0110.0241.0000.0950.0300.0370.0190.0000.1510.0350.3410.0000.1850.1210.027
premium_tech_support0.0480.0580.0720.1640.1640.2730.2130.3330.0000.3360.3860.0000.0160.1190.4140.1000.0210.1200.2240.2940.3540.0360.0490.0951.0000.2790.2760.2780.3250.4370.0950.1880.0300.3940.2510.010
streaming_movies0.1080.0000.0000.0560.0660.1250.1550.4020.0000.4180.4410.0000.0130.1170.6510.2580.0700.0560.1910.2740.1870.2110.1780.0300.2791.0000.8480.5330.2830.5180.0940.1870.0000.4630.3180.000
streaming_music0.1800.1650.0030.0450.0460.0880.1320.3490.0000.3880.3960.0000.0000.0880.5430.1930.0310.0530.1610.2450.1950.1660.1340.0370.2760.8481.0000.4550.2340.4400.0770.1490.0000.3920.2960.014
streaming_tv0.0840.0150.0140.0660.0670.1160.1540.3900.0000.4150.4410.0000.0000.1240.6550.2570.0540.0710.1910.2820.1750.2230.1810.0190.2780.5330.4551.0000.2770.5120.0710.1840.0000.4590.3230.000
tenure_in_months0.0120.0400.0140.1620.1270.4970.4470.3590.0000.0210.0120.013-0.0120.3780.2700.3330.1350.3830.5620.3580.3260.0000.0950.0000.3250.2830.2340.2771.0000.8890.0190.6630.0840.9130.0060.010
total_charges0.0670.0310.0590.0930.0730.3460.3000.5220.0000.4280.290-0.0040.0060.3240.6200.4690.0420.3270.3470.5090.4200.1580.1160.1510.4370.5180.4400.5120.8891.0000.0780.6500.0870.9780.3280.003
total_extra_data_charges0.0310.006-0.0090.0250.0200.0410.0380.0760.0000.1550.089-0.0020.0010.0000.1200.061-0.031-0.0250.0290.1000.0570.0430.0320.0350.0950.0940.0770.0710.0190.0781.000-0.0040.0090.0670.433-0.002
total_long_distance_charges0.0100.0210.6510.1080.0840.3210.2820.2100.0270.0450.0910.008-0.0040.2690.3070.3330.0910.2570.2580.2430.1980.0190.0720.3410.1880.1870.1490.1840.6630.650-0.0041.0000.0610.7780.0290.010
total_refunds0.0200.002-0.0130.0000.0000.0390.0470.0000.0000.0060.000-0.009-0.0090.0310.0280.0460.0180.0370.0160.0000.0310.0000.0140.0000.0300.0000.0000.0000.0840.0870.0090.0611.0000.0820.024-0.004
total_revenue0.0520.0320.2090.1070.0840.3600.3210.4710.0000.3100.2220.0010.0010.3330.5530.4610.0650.3390.3480.4720.3830.1330.1020.1850.3940.4630.3920.4590.9130.9780.0670.7780.0821.0000.2350.007
unlimited_data0.1280.0130.0820.1720.1710.1400.1820.2960.0000.7550.7560.0430.0190.0140.7040.1590.1280.0000.0320.2830.2640.2450.1970.1210.2510.3180.2960.3230.0060.3280.4330.0290.0240.2351.0000.021
zip_code-0.007-0.0080.0060.0770.0640.0240.0420.0000.0140.0430.0270.880-0.7420.027-0.0060.0310.0170.0060.0510.0220.0120.0040.0490.0270.0100.0000.0140.0000.0100.003-0.0020.010-0.0040.0070.0211.000

Missing values

2025-02-25T15:38:16.187078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-25T15:38:16.734747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgenderagemarriednumber_of_dependentscityzip_codelatitudelongitudenumber_of_referralstenure_in_monthsofferphone_serviceavg_monthly_long_distance_chargesmultiple_linesinternet_serviceinternet_typeavg_monthly_gb_downloadonline_securityonline_backupdevice_protection_planpremium_tech_supportstreaming_tvstreaming_moviesstreaming_musicunlimited_datacontractpaperless_billingpayment_methodmonthly_chargetotal_chargestotal_refundstotal_extra_data_chargestotal_long_distance_chargestotal_revenuecustomer_statuschurn_categorychurn_reason
00002-ORFBOFemale37True0Frazier Park9322534.827662-118.99907329not specifiedTrue42.39Falseyescable16.0FalseTrueFalseTrueTrueFalseFalseTrueOne YearTruecredit card65.60593.300.000381.51974.81stayednot specifiednot specified
10003-MKNFEMale46False0Glendale9120634.162515-118.20386909not specifiedTrue10.69Trueyescable10.0FalseFalseFalseFalseFalseTrueTrueFalseMonth-to-MonthFalsecredit card0.00542.4038.331096.21610.28stayednot specifiednot specified
20004-TLHLJMale50False0Costa Mesa9262733.645672-117.92261304offer eTrue33.65Falseyesfiber optic30.0FalseFalseTrueFalseFalseFalseFalseTrueMonth-to-MonthTruebank withdrawal73.90280.850.000134.60415.45churnedCompetitorCompetitor Offer
30011-IGKFFMale78True0Martinez9455338.014457-122.115432113offer dTrue27.82Falseyesfiber optic4.0FalseTrueTrueFalseTrueTrueFalseTrueMonth-to-MonthTruebank withdrawal98.001237.850.000361.661599.51churnedDissatisfactionDissatisfaction
40013-EXCHZFemale75True0Camarillo9301034.227846-119.07990333not specifiedTrue7.38Falseyesfiber optic11.0FalseFalseFalseTrueTrueFalseFalseTrueMonth-to-MonthTruecredit card83.90267.400.00022.14289.54churnedDissatisfactionDissatisfaction
50013-MHZWFFemale23False3Midpines9534537.581496-119.97276209offer eTrue16.77Falseyescable73.0FalseFalseFalseTrueTrueTrueTrueTrueMonth-to-MonthTruecredit card69.40571.450.000150.93722.38stayednot specifiednot specified
60013-SMEOEFemale67True0Lompoc9343734.757477-120.550507171offer aTrue9.96Falseyesfiber optic14.0TrueTrueTrueTrueTrueTrueTrueTrueTwo YearTruebank withdrawal109.707904.250.000707.168611.41stayednot specifiednot specified
70014-BMAQUMale52True0Napa9455838.489789-122.270110863offer bTrue12.96Trueyesfiber optic7.0TrueFalseFalseTrueFalseFalseFalseFalseTwo YearTruecredit card84.655377.800.0020816.486214.28stayednot specifiednot specified
80015-UOCOJFemale68False0Simi Valley9306334.296813-118.68570307offer eTrue10.53Falseyesdsl21.0TrueFalseFalseFalseFalseFalseFalseTrueTwo YearTruebank withdrawal48.20340.350.00073.71414.06stayednot specifiednot specified
90016-QLJISFemale43True1Sheridan9568138.984756-121.345074365not specifiedTrue28.46Trueyescable14.0TrueTrueTrueTrueTrueTrueTrueTrueTwo YearTruecredit card90.455957.900.0001849.907807.80stayednot specifiednot specified
customer_idgenderagemarriednumber_of_dependentscityzip_codelatitudelongitudenumber_of_referralstenure_in_monthsofferphone_serviceavg_monthly_long_distance_chargesmultiple_linesinternet_serviceinternet_typeavg_monthly_gb_downloadonline_securityonline_backupdevice_protection_planpremium_tech_supportstreaming_tvstreaming_moviesstreaming_musicunlimited_datacontractpaperless_billingpayment_methodmonthly_chargetotal_chargestotal_refundstotal_extra_data_chargestotal_long_distance_chargestotal_revenuecustomer_statuschurn_categorychurn_reason
70339975-SKRNRMale24False0Sierraville9612639.559709-120.34563901offer eTrue49.51Falsenonot specifiedNaNFalseFalseFalseFalseFalseFalseFalseFalseMonth-to-MonthFalsecredit card18.9018.900.0049.5168.41joinednot specifiednot specified
70349978-HYCINMale72True1Bakersfield9330135.383937-119.020428147not specifiedTrue42.29Falseyesfiber optic22.0FalseTrueFalseFalseTrueFalseFalseFalseOne YearTruebank withdrawal84.954018.050.0801987.636085.68stayednot specifiednot specified
70359979-RGMZTFemale20False0Los Angeles9002234.023810-118.15658207offer eTrue36.49Falseyesfiber optic42.0FalseTrueFalseFalseTrueTrueTrueTrueOne YearTruecredit card94.05633.450.00255.43888.88stayednot specifiednot specified
70369985-MWVIXFemale53False0Hume9362836.807595-118.90154401offer eTrue42.09Falseyesfiber optic9.0FalseFalseFalseFalseFalseFalseFalseTrueMonth-to-MonthTruecredit card70.1570.150.0042.09112.24churnedCompetitorCompetitor Offer
70379986-BONCEFemale36False0Fallbrook9202833.362575-117.29964404not specifiedTrue2.01Falsenonot specifiedNaNFalseFalseFalseFalseFalseFalseFalseFalseMonth-to-MonthFalsebank withdrawal20.9585.500.008.0493.54churnedCompetitorCompetitor Offer
70389987-LUTYDFemale20False0La Mesa9194132.759327-116.997260013offer dTrue46.68Falseyesdsl59.0TrueFalseFalseTrueFalseFalseTrueTrueOne YearFalsecredit card55.15742.900.00606.841349.74stayednot specifiednot specified
70399992-RRAMNMale40True0Riverbank9536737.734971-120.954271122offer dTrue16.20Trueyesfiber optic17.0FalseFalseFalseFalseFalseTrueTrueTrueMonth-to-MonthTruebank withdrawal85.101873.700.00356.402230.10churnedDissatisfactionDissatisfaction
70409992-UJOELMale22False0Elk9543239.108252-123.64512102offer eTrue18.62Falseyesdsl51.0FalseTrueFalseFalseFalseFalseFalseTrueMonth-to-MonthTruecredit card50.3092.750.0037.24129.99joinednot specifiednot specified
70419993-LHIEBMale21True0Solana Beach9207533.001813-117.263628567offer aTrue2.12Falseyescable58.0TrueFalseTrueTrueFalseTrueTrueTrueTwo YearFalsecredit card67.854627.650.00142.044769.69stayednot specifiednot specified
70429995-HOTOHMale36True0Sierra City9612539.600599-120.636358163not specifiedFalse0.00Falseyescable5.0TrueTrueTrueFalseTrueTrueTrueTrueTwo YearFalsebank withdrawal59.003707.600.000.003707.60stayednot specifiednot specified